The Naval Research Laboratory (NRL) has established a Regional Coastal Oceanography with Nanosatellites (ReCON) project which will explore the ability of high-resolution nanosatellites to monitor coastal, estuarine, riverine, and other maritime environments in support of U.S. Navy operations. The project will initially focus on using data from the almost 150+ Planet “Dove” nanosatellites which fly in “flocks” acquiring remotely sensed data from sunlight reflecting off the earth surface. The usefulness of remotely sensed data within our research and operations is determined by the ability to accurately perform atmospheric correction and compute water leaving radiances (Lw), which are then normalized (nLw) and form the basis for the generation of remote sensing reflectance and other inherent and apparent optical property products. These nanosatellites have a single infrared band, although two such bands are typically required to automatically select an appropriate aerosol model during atmospheric correction, prior to estimating nLw. While early in the project, this initial study will assess nanosatellite capabilities to accurately retrieve nLw measurements by specifying the aerosol model selection during the atmospheric correction process. Here we present nLw retrievals for a variety of Planet nanosatellite imagery covering an entire year over a northern island of Venezuela, which covers coastal and open ocean type waters. The nLw retrievals from the nanosatellites using forced aerosol models are compared to coincident nLw retrievals from the Suomi-National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to gauge the potential reliability and accuracy of using nanosatellite imagery as a competent data source for ocean color optics.
In September, 2018, Hurricane Florence made landfall in North Carolina as a Category 1 hurricane and inundated the eastern United States with significant rainfall. Precipitation from this slow moving storm event caused massive flooding. Outflow from this flooding carried suspended solids including sediments and other particulates as the rainwater worked its way through river and watershed systems toward the Atlantic Ocean. The Advanced Baseline Imager (ABI) on the NOAA Geostationary Operational Environmental Satellite - 16 (GOES-16) monitors the eastern United States. ABI data from GOES-16 is available every 5 minutes and provides a platform for studying the increased volume of river flow into the Atlantic Ocean. Data from the GOES-16 ABI covering the Atlantic waters off the eastern United States were downloaded after the Hurricane Florence event. Methodologies for atmospheric correction were used to generate water leaving radiance values from the GOES-16 ABI data sets. Using the multiple looks per day, the plumes of suspended solids were delineated and studied.
Herein we present an initial approach for assessing water color, specifically chromaticity, and determining if an accurate correlation can be made within chromaticity space between the water color and a hyperspectral synthetic data set. The water color assessed in this paper consist of remote sensing reflectance (Rrs) distributions from the Suomi-National Polarorbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and the hyperspectral synthetic data set consists of Rrs distributions of natural marine waters. Where strong correlations exist, the hyperspectral Rrs reference data can be blended into the SNPP VIIRS Rrs data, thus creating a hyperspectral SNPP VIIRS spectra. Where applicable, the newly constructed VIIRS hyperspectral signature is compared to in situ data taken during a 2018 National Oceanic and Atmospheric Administration (NOAA) Calibration/Validation cruise. Given the proliferation of small, low-cost airborne platforms equipped with color imaging cameras, there exists tremendous potential to use and hyperspectrally enhance these data streams for ocean monitoring and scientific research. However, techniques for extracting traditional ocean radiant spectra from RGB data fields are new to oceanographic disciplines.
Boreal winter meteorological fronts manifest across the northern Gulf of Mexico as rapid 10-15° C drops in air temperature and accelerating northerly winds. The physical coastal ocean response across the Louisiana-Texas (LATEX) continental shelf system involves a complex interplay between coastal buoyancy, wind forcing, and intense thermal energy fluxes out of the ocean. Herein we combine numerical simulations, in situ optical surveys, and coincident satellite images derived from the Ocean and Land Colour Imager (OLCI) and other sensors to further unravel the mechanistic functioning and optical signatures of these complex events. The conspicuous optical gradients evident in color satellite images coincident with cold air outbreak (CAO) events appear to result from surface ventilation of sediment-laden bottom waters and wind/buoyancy-driven surface currents. The hyperspectral gradients associated with water mass types (sediment resuspension in marine waters versus freshwater effluent plumes) give rise to true color gradients that may be tracked with low spectral resolution color sensors at very high temporal resolution.
The combination of increased spectral resolution for in situ ocean optical instrumentation as well as future ocean remote sensing missions (e.g., PACE) provides an opportunity to examine new methods of analysis and ocean monitoring that were not feasible during the multispectral satellite era. For example, hyperspectral data enables a much more precise determination of the apparent true color for natural waters, one based on the full spectral shape of water-leaving radiance distributions. Herein we provide examples of how specific integrated biogeo-optical and physical processes in the northern Gulf of Mexico have characteristic hyperspectral signatures, and thusly, characteristic true color identifiers. Our emergent hypothesis is that once the characteristic hyperspectral color signature of a specific biophysical process is known, it can be detected and monitored even with multispectral or broad-band response digital imaging systems. To test this hypothesis, we examine archived imagery from MODIS and HICO to identify putative bottom boundary layer ventilation events along divergent shelf-frontal boundaries across the northern Gulf continental margin. Whereas on-demand in situ physical data that provide spatiotemporal correspondence with archived images are not available, we employ the data-assimilative Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) as a physical data surrogate. Preliminary results of this method appear to support the hypothesis, with the caveat that model results must be interpreted with due caution.
We describe an approach to produce short-term (1- to 3-day) forecasts of bio-optical properties by coupling moderate-resolution imaging spectroradiometer satellite (MODIS) ocean color imagery with a hydrodynamic model. The bio-optical property (chlorophyll in this case) is treated as a conservative tracer; the satellite distribution is advected forward in time using the current field from the hydrodynamic model. Uncertainties in both the satellite chlorophyll values and the currents from the circulation model impact the final forecast; we apply ensemble techniques to quantify the errors separately and in combination. For the ocean color imagery, we further apply ensemble techniques to partition the chlorophyll uncertainties into components due to atmospheric correction and bio-optical inversion, by applying noise to the near-infrared and visible band sets separately. The standard deviation for each ensemble suite provides an indication of uncertainty, or confidence in the satellite chlorophyll values and the hydrodynamic model current fields. By combining the two ensemble sets, we produce a final chlorophyll forecast field and associated uncertainty map that include both sets of uncertainties. We examine mean and individual forecast ensemble members (spread-skill statistics, RMS differences) to assess predictive value. This work represents a significant advancement in representing errors associated with satellite ocean color imagery and bio-optical forecasts.
We propose a methodology to quantify errors and produce uncertainty maps for satellite-derived ocean color bio-optical
products using ensemble simulations. Ensemble techniques have been used by the environmental numerical modeling
community to propagate initialization, forcing, and algorithm error sources through-out the full simulation process, but
similar approaches have not yet been applied to satellite optical properties. Uncertainties in retrievals of bio-optical
properties from satellite ocean color imagery are related to a variety of factors, including sensor calibration, atmospheric
correction, and the bio-optical inversion algorithms. Errors propagate, amplify, and intertwine along the processing path,
so it is important to understand how the errors cascade through each step of the analysis, to assess their impact and
identify the main factors contributing to the uncertainties in the final products. Also, we are interested in producing
short-term forecasts of the bio-optical property distributions, by coupling the satellite imagery with physical circulation
models. So, in addition to the uncertainties in the satellite-derived bio-optical properties due to the above-mentioned
factors, the uncertainties in the model currents used to advect the bio-optical properties add another layer of complexity
to the problem. We outline these processes and present preliminary results for this approach.
We examine the impact of incorrect atmospheric correction, specifically incorrect aerosol model selection, on
retrieval of bio-optical properties from satellite ocean color imagery. Uncertainties in retrievals of bio-optical properties
(such as chlorophyll, absorption and backscattering coefficients) from satellite ocean color imagery are related to a
variety of factors, including errors associated with sensor calibration, atmospheric correction, and the bio-optical
inversion algorithms. In many cases, selection of an inappropriate or erroneous aerosol model during atmospheric
correction can dominate the errors in the satellite estimation of the normalized water-leaving radiances (nLw), especially over turbid, coastal waters. These errors affect the downstream bio-optical properties. Here, we focus on only the
impact of incorrect aerosol model selection on the nLw radiance estimates, through comparisons between Moderate-
Resolution Imaging Spectroradiometer (MODIS) satellite data and in situ measurements from AERONET-OC (Aerosol
Robotic NETwork - Ocean Color) sampling platforms.
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